R Notebook
Results of the crossed DE tests (Peri vs white, Supra vs white, Subq vs brown, Visce vs brown). Supra4 and Subq4 were removed from the DE analyses.
library(VennDiagram)## Loading required package: grid## Loading required package: futile.loggerlibrary(Seurat)## Loading required package: ggplot2## Loading required package: cowplot##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggplot2':
##
## ggsave## Loading required package: Matrixload('../../data/markergenes/markergenes-crossed.negbinom.supra4-subq4-removed')
markers <- df.cluster_markersMarker genes of the intersection of Supra and Peri. Supra has 111 marker genes, Peri 54. They have 15 genes in common.
genes.peri <- markers[markers$cluster == 'Peri.white', 'gene']
genes.supra <- markers[markers$cluster == 'Supra.white', 'gene']
peri.supra <- intersect(genes.peri, genes.supra)
grid.newpage()
draw.pairwise.venn(length(genes.peri), length(genes.supra), length(peri.supra), category=c('Peri', 'Supra'), lty=rep('blank', 2), fill=c('#f67770', '#1fbfc3'), scaled=T, cat.fontfamily = rep("helvetica", 2))
## (polygon[GRID.polygon.11], polygon[GRID.polygon.12], polygon[GRID.polygon.13], polygon[GRID.polygon.14], text[GRID.text.15], text[GRID.text.16], text[GRID.text.17], text[GRID.text.18], text[GRID.text.19])Marker genes of the intersection of Subq and Visce. Subq has 91 marker genes, Visce 11. They have only 2 genes in common.
genes.visce <- markers[markers$cluster == 'Visce.brown', 'gene']
genes.subq <- markers[markers$cluster == 'Subq.brown', 'gene']
visce.subq <- intersect(genes.visce, genes.subq)
grid.newpage()
draw.pairwise.venn(length(genes.visce), length(genes.subq), length(visce.subq), category=c('Visce', 'Subq'), lty=rep('blank', 2), fill=c('#c680fc', '#7dac1f'), scaled=T, cat.fontfamily = rep("helvetica", 2))
## (polygon[GRID.polygon.20], polygon[GRID.polygon.21], polygon[GRID.polygon.22], polygon[GRID.polygon.23], text[GRID.text.24], text[GRID.text.25], text[GRID.text.26], lines[GRID.lines.27], text[GRID.text.28], text[GRID.text.29])all10x <- readRDS('../../data/10x')Markers intersection Peri and Supra
print(peri.supra)## [1] "TM4SF1" "CRYAB" "PTX3" "ACAN" "SERTAD4-AS1"
## [6] "MYL9" "MGST3" "RPSA" "LY6K" "MTRNR2L8"
## [11] "NPM1" "RPS26" "FHL2" "HSPA8" "PENK"markers[which(markers$gene %in% peri.supra),]markers.peri.supra <- unique(markers[which(markers$gene %in% peri.supra),'gene'])VlnPlot(all10x, features.plot=markers.peri.supra, group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
Average expressions
avg_expr <- AverageExpression(SetAllIdent(all10x, 'sample_name2'), genes.use=markers.peri.supra)## Finished averaging RNA for cluster Peri## Finished averaging RNA for cluster Subq## Finished averaging RNA for cluster Supra## Finished averaging RNA for cluster Visceprint(avg_expr)## Peri Subq Supra Visce
## TM4SF1 3.4316696 0.6391543 1.6205215 1.06893693
## CRYAB 11.6788632 9.0158322 8.1057384 4.33485406
## PTX3 8.9524847 2.7552232 5.4187969 5.89241882
## ACAN 0.9768580 0.8079589 0.7670946 0.05396552
## SERTAD4-AS1 0.7205134 0.1680772 0.5134078 0.20174314
## MYL9 8.1017100 7.6865607 10.3668852 5.12803602
## MGST3 3.6997098 2.7351204 3.7378162 2.40865940
## RPSA 9.7315871 12.2151636 17.3850348 9.07224717
## LY6K 0.9675921 0.3592519 1.0744199 0.47868410
## MTRNR2L8 2.3004938 1.0144049 2.3484701 1.56926052
## NPM1 11.1950728 9.1229474 12.6994009 8.17296772
## RPS26 12.9303438 12.3515888 11.0831555 8.80135083
## FHL2 4.4227906 2.7773847 4.6096996 3.44791982
## HSPA8 4.6952899 3.4865837 4.8627431 3.34559620
## PENK 1.0213435 1.0261695 1.7821727 0.12672707Markers intersection Subq and Visce
print(visce.subq)## [1] "HOXB7" "GREM1"print(markers[which(markers$gene %in% visce.subq),])## cluster p_val avg_logFC pct.1 pct.2 p_val_adj gene
## 203 Visce.brown 3.746971e-50 0.2529170 0.540 0.343 9.021956e-46 HOXB7
## 205 Visce.brown 8.453013e-26 0.3361652 0.771 0.713 2.035316e-21 GREM1
## 215 Subq.brown 0.000000e+00 0.6455935 0.922 0.713 0.000000e+00 GREM1
## 229 Subq.brown 1.312154e-255 0.2938827 0.614 0.343 3.159405e-251 HOXB7VlnPlot(all10x, features.plot=toupper(c('GREM1', 'HOXB7')), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
Average expressions
avg_expr <- AverageExpression(SetAllIdent(all10x, 'sample_name2'), genes.use=c('GREM1', 'HOXB7'))## Finished averaging RNA for cluster Peri## Finished averaging RNA for cluster Subq## Finished averaging RNA for cluster Supra## Finished averaging RNA for cluster Visceprint(avg_expr)## Peri Subq Supra Visce
## GREM1 2.7527861 3.849341 0.99336501 3.0547160
## HOXB7 0.4342412 0.598484 0.04638687 0.5897077Top markers Peri
print(markers[markers$cluster == 'Peri.white',][1:20,])## cluster p_val avg_logFC pct.1 pct.2 p_val_adj gene
## 1 Peri.white 0 0.8700187 0.963 0.655 0 SRGN
## 2 Peri.white 0 0.8337517 0.928 0.426 0 TM4SF1
## 3 Peri.white 0 0.6715377 0.994 0.960 0 CRYAB
## 4 Peri.white 0 0.6681469 0.830 0.431 0 MEST
## 5 Peri.white 0 0.6314312 0.564 0.183 0 NEFM
## 6 Peri.white 0 0.6023525 0.961 0.805 0 PTX3
## 7 Peri.white 0 0.5681777 0.932 0.662 0 RGS4
## 8 Peri.white 0 0.5420506 0.698 0.404 0 MTRNR2L1
## 9 Peri.white 0 0.4967890 0.999 0.981 0 HSP90AA1
## 10 Peri.white 0 0.4948082 0.967 0.813 0 GLIPR1
## 11 Peri.white 0 0.4855498 0.917 0.660 0 CTSC
## 12 Peri.white 0 0.4615908 0.866 0.670 0 KRT18
## 13 Peri.white 0 0.4529379 0.375 0.135 0 KISS1
## 14 Peri.white 0 0.4414635 0.999 0.968 0 TAGLN
## 15 Peri.white 0 0.3857197 0.592 0.198 0 ACAN
## 16 Peri.white 0 0.3764161 1.000 0.987 0 COTL1
## 17 Peri.white 0 0.3638208 0.956 0.799 0 SCUBE3
## 18 Peri.white 0 0.3607074 0.999 0.984 0 HMGA1
## 19 Peri.white 0 0.3596772 0.645 0.213 0 SERTAD4-AS1
## 20 Peri.white 0 0.3591175 0.823 0.583 0 FGF5VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Peri.white',][1:20,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
Average expressions
avg_expr <- AverageExpression(SetAllIdent(all10x, 'sample_name2'), genes.use=as.vector(markers[markers$cluster == 'Peri.white',][1:20,'gene']))## Finished averaging RNA for cluster Peri## Finished averaging RNA for cluster Subq## Finished averaging RNA for cluster Supra## Finished averaging RNA for cluster Visceprint(avg_expr)## Peri Subq Supra Visce
## SRGN 5.8206736 2.0350813 2.8006023 2.02438133
## TM4SF1 3.4316696 0.6391543 1.6205215 1.06893693
## CRYAB 11.6788632 9.0158322 8.1057384 4.33485406
## MEST 2.4997998 0.4468707 0.5026227 1.18282505
## NEFM 1.3840622 0.1874133 0.1864657 0.27597404
## PTX3 8.9524847 2.7552232 5.4187969 5.89241882
## RGS4 4.1922050 1.2298838 1.1565053 2.29335514
## MTRNR2L1 1.3492361 0.2270019 0.4104273 0.41064271
## HSP90AA1 11.8954314 7.0363837 6.9802059 6.82814503
## GLIPR1 3.9064008 1.8824276 1.8335648 1.83318503
## CTSC 2.7840898 0.7660410 0.9131338 1.77476344
## KRT18 4.5239395 4.0328885 3.8174935 2.78346511
## KISS1 1.0199106 0.1511564 0.2328579 0.33890336
## TAGLN 16.4996665 16.5636903 17.9518409 8.71604012
## ACAN 0.9768580 0.8079589 0.7670946 0.05396552
## COTL1 8.2366504 4.2785718 3.8596950 5.16058240
## SCUBE3 5.5537247 4.0320897 3.2102049 2.41259654
## HMGA1 11.9338014 5.8965800 6.5937263 8.14545805
## SERTAD4-AS1 0.7205134 0.1680772 0.5134078 0.20174314
## FGF5 2.2099574 1.3040271 1.0019012 0.92095516Top markers Supra
print(markers[markers$cluster == 'Supra.white',][1:20,])## cluster p_val avg_logFC pct.1 pct.2 p_val_adj gene
## 70 Supra.white 0 2.1583210 0.730 0.146 0 IGFBP5
## 71 Supra.white 0 1.0851716 0.987 0.832 0 IGFBP3
## 72 Supra.white 0 0.7884681 0.981 0.808 0 AKAP12
## 73 Supra.white 0 0.6945874 0.774 0.433 0 TNFRSF11B
## 74 Supra.white 0 0.6845614 0.903 0.513 0 IFI27
## 75 Supra.white 0 0.6784346 0.898 0.523 0 PPAP2B
## 76 Supra.white 0 0.6674789 0.760 0.382 0 EFEMP1
## 77 Supra.white 0 0.6591687 0.994 0.877 0 MFAP5
## 78 Supra.white 0 0.6527350 0.580 0.142 0 G0S2
## 79 Supra.white 0 0.6478446 0.943 0.687 0 DCN
## 80 Supra.white 0 0.6370642 0.875 0.615 0 CYP1B1
## 81 Supra.white 0 0.5793271 0.992 0.983 0 IGFBP6
## 82 Supra.white 0 0.5775069 0.981 0.889 0 COL6A2
## 83 Supra.white 0 0.5583988 0.902 0.568 0 JUNB
## 84 Supra.white 0 0.5299842 0.799 0.485 0 FOS
## 85 Supra.white 0 0.5295200 0.954 0.799 0 CTGF
## 86 Supra.white 0 0.5223843 0.953 0.766 0 CRLF1
## 87 Supra.white 0 0.5220216 0.979 0.705 0 MTRNR2L8
## 88 Supra.white 0 0.5190278 0.617 0.223 0 LXN
## 89 Supra.white 0 0.5169242 0.973 0.933 0 FBN1VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Supra.white',][1:20,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
Average expressions
avg_expr <- AverageExpression(SetAllIdent(all10x, 'sample_name2'), genes.use=as.vector(markers[markers$cluster == 'Supra.white',][1:20,'gene']))## Finished averaging RNA for cluster Peri## Finished averaging RNA for cluster Subq## Finished averaging RNA for cluster Supra## Finished averaging RNA for cluster Visceprint(avg_expr)## Peri Subq Supra Visce
## IGFBP5 0.7489022 3.0983954 15.4306062 0.5815240
## IGFBP3 1.7325738 6.1283132 14.1339730 3.8170588
## AKAP12 2.5056396 1.2846838 6.8558457 3.8544849
## TNFRSF11B 1.1039258 1.4580169 2.6214672 0.4841851
## IFI27 1.0512808 1.3286708 3.6114980 1.7574614
## PPAP2B 0.9297630 0.7662966 1.9012818 0.7326293
## EFEMP1 0.3814137 0.9818796 2.1704064 0.5674456
## MFAP5 2.9391056 4.0538299 7.1853852 3.1858328
## G0S2 0.2851369 0.3581771 1.2123740 0.2615178
## DCN 1.1348387 1.9135496 4.9482015 2.6174980
## CYP1B1 2.6679570 2.3677300 5.4073672 2.9356624
## IGFBP6 5.9974532 5.3595788 11.0979197 8.3656315
## COL6A2 2.9118379 1.9736643 3.5610825 2.0010397
## JUNB 0.8092611 0.5531405 1.6844448 0.9698807
## FOS 0.5389503 0.9343437 1.9137151 0.8264156
## CTGF 5.3583618 5.4797260 7.2848786 2.9849238
## CRLF1 0.7869020 0.9085457 2.6842333 2.0705012
## MTRNR2L8 2.3004938 1.0144049 2.3484701 1.5692605
## LXN 0.2719844 0.1458685 0.8563465 0.2308667
## FBN1 2.7767017 3.3825989 6.2673388 3.4382746Top markers Visce
print(markers[markers$cluster == 'Visce.brown',][1:20,])## cluster p_val avg_logFC pct.1 pct.2 p_val_adj
## 196 Visce.brown 0.000000e+00 0.3428612 1.000 1.000 0.000000e+00
## 197 Visce.brown 2.181113e-278 0.2504584 0.325 0.003 5.251685e-274
## 198 Visce.brown 1.552694e-261 0.4773603 1.000 1.000 3.738576e-257
## 199 Visce.brown 1.739342e-219 0.4227270 0.588 0.409 4.187987e-215
## 200 Visce.brown 4.905554e-139 0.2915473 0.546 0.396 1.181159e-134
## 201 Visce.brown 5.156757e-133 0.2963781 0.400 0.247 1.241644e-128
## 202 Visce.brown 6.905548e-102 0.2602546 0.838 0.913 1.662718e-97
## 203 Visce.brown 3.746971e-50 0.2529170 0.540 0.343 9.021956e-46
## 204 Visce.brown 8.626536e-29 0.2906204 0.595 0.465 2.077097e-24
## 205 Visce.brown 8.453013e-26 0.3361652 0.771 0.713 2.035316e-21
## 206 Visce.brown 6.221691e-11 0.4410880 0.863 0.862 1.498059e-06
## 207 Visce.brown 2.387362e-04 0.2557619 0.464 0.372 1.000000e+00
## 208 Visce.brown 3.884867e-02 0.2659491 0.635 0.587 1.000000e+00
## NA <NA> NA NA NA NA NA
## NA.1 <NA> NA NA NA NA NA
## NA.2 <NA> NA NA NA NA NA
## NA.3 <NA> NA NA NA NA NA
## NA.4 <NA> NA NA NA NA NA
## NA.5 <NA> NA NA NA NA NA
## NA.6 <NA> NA NA NA NA NA
## gene
## 196 FTH1
## 197 BARX1
## 198 FTL
## 199 LUM
## 200 HOXA5
## 201 SFRP1
## 202 GLRX
## 203 HOXB7
## 204 RP11-173B14.5
## 205 GREM1
## 206 SERPINE2
## 207 FAM43A
## 208 THBS2
## NA <NA>
## NA.1 <NA>
## NA.2 <NA>
## NA.3 <NA>
## NA.4 <NA>
## NA.5 <NA>
## NA.6 <NA>VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Visce.brown',][,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
Average expressions
avg_expr <- AverageExpression(SetAllIdent(all10x, 'sample_name2'), genes.use=as.vector(markers[markers$cluster == 'Visce.brown',][,'gene']))## Finished averaging RNA for cluster Peri## Finished averaging RNA for cluster Subq## Finished averaging RNA for cluster Supra## Finished averaging RNA for cluster Visceprint(avg_expr)## Peri Subq Supra Visce
## FTH1 1.032583e+02 9.359533e+01 8.559383e+01 154.4401017
## BARX1 3.478593e-04 1.017664e-04 1.360098e-03 0.2860095
## FTL 9.807126e+01 1.355686e+02 1.149756e+02 154.2476036
## LUM 2.810574e-01 7.806584e-01 5.983495e-01 1.0551704
## HOXA5 3.334031e-01 1.292788e-01 1.250488e-01 0.6570014
## SFRP1 2.492295e-01 1.430866e-01 1.034199e-01 0.5736304
## GLRX 2.108369e+00 1.549312e+00 1.538773e+00 2.4907010
## HOXB7 4.342412e-01 5.984840e-01 4.638687e-02 0.5897077
## RP11-173B14.5 4.921283e-01 1.446347e-01 1.266281e-01 0.7655605
## GREM1 2.752786e+00 3.849341e+00 9.933650e-01 3.0547160
## SERPINE2 3.153295e+00 3.861648e+00 3.783776e+00 5.1276686
## FAM43A 3.881069e-01 2.026659e-01 2.143426e-01 0.7074830
## THBS2 4.339457e-01 4.939721e-01 1.095348e+00 1.0288454Top markers Subq
print(markers[markers$cluster == 'Subq.brown',][1:20,])## cluster p_val avg_logFC pct.1 pct.2 p_val_adj
## 209 Subq.brown 0.000000e+00 1.2831116 0.897 0.710 0.000000e+00
## 210 Subq.brown 0.000000e+00 1.0710639 1.000 1.000 0.000000e+00
## 211 Subq.brown 0.000000e+00 1.0433224 0.985 0.947 0.000000e+00
## 212 Subq.brown 0.000000e+00 0.9901528 0.509 0.093 0.000000e+00
## 213 Subq.brown 0.000000e+00 0.7693235 0.855 0.743 0.000000e+00
## 214 Subq.brown 0.000000e+00 0.6620112 0.881 0.753 0.000000e+00
## 215 Subq.brown 0.000000e+00 0.6455935 0.922 0.713 0.000000e+00
## 216 Subq.brown 0.000000e+00 0.6268227 0.565 0.431 0.000000e+00
## 217 Subq.brown 0.000000e+00 0.5003979 0.311 0.078 0.000000e+00
## 218 Subq.brown 0.000000e+00 0.3674380 0.457 0.226 0.000000e+00
## 219 Subq.brown 0.000000e+00 0.3661562 0.551 0.332 0.000000e+00
## 220 Subq.brown 0.000000e+00 0.3056729 1.000 1.000 0.000000e+00
## 221 Subq.brown 0.000000e+00 0.2870511 1.000 1.000 0.000000e+00
## 222 Subq.brown 0.000000e+00 0.2666346 1.000 1.000 0.000000e+00
## 223 Subq.brown 0.000000e+00 0.2645295 0.426 0.076 0.000000e+00
## 224 Subq.brown 3.122433e-301 0.3503707 0.713 0.701 7.518193e-297
## 225 Subq.brown 1.090240e-299 0.4407373 0.705 0.507 2.625079e-295
## 226 Subq.brown 1.441828e-288 0.3504987 1.000 0.999 3.471634e-284
## 227 Subq.brown 1.596441e-259 0.3904153 1.000 1.000 3.843911e-255
## 228 Subq.brown 6.835613e-259 0.4182473 0.637 0.459 1.645879e-254
## gene
## 209 THBS1
## 210 RPS29
## 211 TIMP3
## 212 BCYRN1
## 213 DKK1
## 214 AC009501.4
## 215 GREM1
## 216 HES1
## 217 ACTC1
## 218 TRNP1
## 219 NMT1
## 220 RPL37
## 221 RPL26
## 222 RPL39
## 223 NRN1
## 224 FAM101B
## 225 TINAGL1
## 226 RPL38
## 227 RPL37A
## 228 AAK1Most of the markers for Subq seem to be found because Subq_3 stands out (RPS29, BCYRN1, RPL36A, ATP5I, AC009501.4, RPS10). Mostly ribosomal genes.
BCYRN1 = brain cytoplasmic RNA
VlnPlot(all10x, features.plot=as.vector(markers[markers$cluster == 'Subq.brown',][1:20,'gene']), group.by='sample_name', point.size.use=-1, nCol=2, x.lab.rot=T, size.x.use=10)
Average expressions
avg_expr <- AverageExpression(SetAllIdent(all10x, 'sample_name2'), genes.use=as.vector(markers[markers$cluster == 'Subq.brown',][1:20,'gene']))## Finished averaging RNA for cluster Peri## Finished averaging RNA for cluster Subq## Finished averaging RNA for cluster Supra## Finished averaging RNA for cluster Visceprint(avg_expr)## Peri Subq Supra Visce
## THBS1 1.48527467 6.8415807 1.64058118 7.553478e-01
## RPS29 13.48926802 31.0426185 10.99365342 1.356076e+01
## TIMP3 2.48177489 11.8900982 5.40626711 3.614699e+00
## BCYRN1 0.04241281 1.3272326 0.02940310 5.666471e-02
## DKK1 2.30323192 4.8188376 1.55563626 1.802521e+00
## AC009501.4 0.71374598 1.5554074 0.35777013 7.799085e-01
## GREM1 2.75278615 3.8493407 0.99336501 3.054716e+00
## HES1 0.26871472 1.1641241 0.33257699 3.551162e-01
## ACTC1 0.01788283 0.6644584 0.13526928 6.977442e-02
## TRNP1 0.10618125 0.4383158 0.07311753 1.007811e-01
## NMT1 0.15103749 0.5967743 0.20234138 1.373709e-01
## RPL37 27.37097566 29.5928548 20.61540299 2.826035e+01
## RPL26 22.38053430 32.3717676 24.65098797 2.733704e+01
## RPL39 27.20132022 30.8047060 22.51210324 2.824151e+01
## NRN1 0.06518806 0.2772627 0.02120344 4.633634e-04
## FAM101B 0.65001788 0.9534913 0.41543600 4.357017e-01
## TINAGL1 0.59335199 0.9988498 0.31014318 4.057586e-01
## RPL38 15.77256635 17.3496776 10.69920877 1.501471e+01
## RPL37A 42.66949757 50.2723200 32.36711933 4.390718e+01
## AAK1 0.27706620 0.6922202 0.18456739 2.559899e-01